#!/usr/bin/python
# -*- encoding: utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.ops import rnn, rnn_cell

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

hm_epochs = 10
n_classes = 10
batch_size = 128
chunk_size = 28
n_chunks = 28
rnn_size = 128

x = tf.placeholder('float', [None, n_chunks, chunk_size])
y = tf.placeholder('float')


def recurrent_neural_network(x):
    layer = {'weights': tf.Variable(tf.random_normal([rnn_size, n_classes])),
             'biases': tf.Variable(tf.random_normal([n_classes]))}

    x = tf.transpose(x, [1, 0, 2])
    x = tf.reshape(x, [-1, chunk_size])
    x = tf.split(0, n_chunks, x)

    lstm_cell = rnn_cell.BasicLSTMCell(rnn_size)
    outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)

    output = tf.matmul(outputs[-1], layer['weights']) + layer['biases']

    return output


def train_neural_network(x):
    prediction = recurrent_neural_network(x)
    # OLD VERSION:
    # cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
    # NEW:
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    with tf.Session() as sess:
        # OLD:
        # sess.run(tf.initialize_all_variables())
        # NEW:
        sess.run(tf.global_variables_initializer())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            for _ in range(int(mnist.train.num_examples / batch_size)):
                epoch_x, epoch_y = mnist.train.next_batch(batch_size)
                epoch_x = epoch_x.reshape((batch_size, n_chunks, chunk_size))

                _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y})
                epoch_loss += c

            print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss)

        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))

        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print('Accuracy:', accuracy.eval({x: mnist.test.images.reshape((-1,n_chunks,chunk_size)), y: mnist.test.labels}))


train_neural_network(x)